Longitudinal follow‐up and performance validation of an mRNA‐based urine test (Xpert <sup>®</sup> Bladder Cancer Monitor ) for surveillance in patients with non‐muscle‐invasive bladder cancer
Bibliographic record
Abstract
OBJECTIVE: To evaluate the performance of the Xpert Bladder Cancer Monitor (Xpert; Cepheid, Sunnyvale, CA, USA) test as a predictor of tumour recurrence in patients with non-muscle-invasive bladder cancer (NMIBC). PATIENTS AND METHODS: Patients (n = 429) undergoing surveillance for NMIBC underwent Xpert, cytology, and UroVysion testing. Patients with a positive Xpert and a negative cystoscopy result (positive-negative [PN] group, n = 66) and a control group of double negative patients (negative Xpert and cystoscopy results [NN] group) were followed for 12 months (±90 days). RESULTS: Histology-confirmed recurrences were detected in 58 patients (13.5%). Xpert had an overall sensitivity of 60.3% and a specificity of 76.5%. The sensitivity for high-grade (HG) cancer was 87% with a negative predictive value (NPV) of 99%. Urine cytology showed an overall sensitivity of 23.2% (47.6% sensitivity for HG tumours) and a specificity of 88.3%. In the PN group, 32% (n = 21) developed a recurrence within 12 months, 11 of which were HG tumours. In the NN control group, 14% (n = 9) developed a recurrence and only two were HG tumours. The hazard ratio for developing recurrence in the PN group was 2.68 for all tumours and 6.84 for HG cancer. CONCLUSIONS: The Xpert test has a high sensitivity for detecting the recurrence of cancer and a high NPV for excluding HG cancer. In addition, the data suggest that patients with a positive Xpert assay in the setting of negative cystoscopy are at high risk for recurrence and need close surveillance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".